Method of multiuser precoding and scheduling and base station for implementing the same

ABSTRACT

The present invention provides a method of multiuser precoding and scheduling, comprising: feeding channel state information (CSI) and statistic properties of CSI error back to a base station (BS) from a user equipment (UE); generating multiuser precoding matrix and scheduling scheme by the BS, according to the feedback CSI and the statistic properties of CSI error; and performing multiuser precoding and scheduling on user data by using the generated multiuser precoding matrix and scheduling scheme.

BACKGROUND OF THE INVENTION

1. FIELD OF INVENTION

The present invention relates to radio Multiple Input Multiple Output(MIMO) communication system, and particularly to method of multiuser(MU) precoding and scheduling, which can generate multiuser precodingmatrix and scheduling scheme for multiuser precoding and scheduling byusing channel state information (CSI, which may be inaccurate due tovarious factors such as feedback quantization, feedback delay and thelike) and statistic properties of CSI error fed back from a user side.

2. DESCRIPTION OF PRIOR ART

Recently, MU-MIMO has attracted much attention due to its advantage incapacity as well as the ability to work with single-antenna users (SU)and still maintain MIMO advantages.

Compared with SU-MIMO, the transmission processing of MU-MIMO iscomplicated due to the fact that each user must decode its messagesindependently without joint operation with other users. The core problemis how to solve co-channel interference (CCI) among users.

To solve this problem, multiuser precoding techniques are used in theMU-MIMO system to control or totally avoid CCI among users, so that eachuser suffers no or only limited interference from other users. For atotal avoidance or effective control of CCI, full CSI for all users isrequired at the transmitter, which is only an assumption impractical toactual systems. In practice, full CSI is difficult to be achieved, andthus imperfect CSI is always used at the transmitter. With suchimperfect CSI, the CCI among users cannot be totally avoided even usingzero-forcing-type precoding algorithms. The residual CCI due toimperfect CSI cannot be suppressed at the receiver by commoninterference-suppression methods such as maximum-likelihood (ML) orminimum-mean-square-error (MMSE) detection. As a result, the CCI canonly be regarded as additive noise whose average power grows with anincrease in the total transmission power. This characteristic of the CCIrestricts significantly the performance of MU-MIMO, especially at highSNRs.

SUMMARY OF THE INVENTION

In order to address deterioration of MU-MIMO performance caused by theresidual CCI due to imperfect CSI at the transmitter, the presentinvention provides a method of multiuser precoding and scheduling inwhich multiuser precoding matrix and scheduling scheme for multiuserprecoding and scheduling is generated by using CSI information andstatistic properties of CSI error fed back from a user. The precodingmatrix and scheduling scheme thus generated can adapt to a situationthat channel information at the transmitter is imperfect, reduce theresidue CCI, and thus reduce the deterioration of MU-MIMO performancedue to imperfect CSI.

An object of the present invention is to provide a method of multiuserprecoding and scheduling, comprising: feeding CSI and statisticproperties of CSI error back to a base station (BS) from a userequipment (UE); generating, at the BS, multiuser precoding matrix andscheduling scheme according to the feedback CSI and the statisticproperties of CSI error; and performing multiuser precoding andscheduling on user data by using the generated multiuser precodingmatrix and scheduling scheme.

Preferably, the CSI is an estimation of a channel matrix.

Preferably, the statistic properties of CSI error comprise a covariancematrix of errors of the estimation of the channel matrix.

Preferably, the multiuser precoding uses a minimum-mean-square-error(MMSE)-type algorithm.

Preferably, the MMSE-type algorithm is a successive MMSE algorithm.

Preferably, the multiuser scheduling uses a capacity-maximizationcriterion.

Preferably, the statistic properties of the CSI error are obtained bymeasuring channel estimation error, feedback error and quantizationerror.

Preferably, the method is used in a MU-MIMO communication system.

The present invention also provides a base station, comprising: areception device for receiving CSI and statistic properties of CSI errorwhich are fed back from a UE; a multiuser precoding matrix andscheduling scheme generation device for to generating multiuserprecoding matrix and scheduling scheme according to the feedback CSI andthe statistic properties of CSI error; and a multiuser precoding andscheduling device for performing multiuser precoding and scheduling onuser data by using the generated multiuser precoding matrix andscheduling scheme.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, advantages and characteristics of the present inventionwill be more apparent, according to descriptions of preferredembodiments in connection with the drawings, wherein:

FIG. 1 is a flow chart of a method of multiuser precoding and schedulingaccording to the present invention;

FIG. 2 is a block diagram of a BS for implementing the method ofmultiuser precoding and scheduling according to the present invention;and

FIGS. 3 and 4 are graphs of performance comparison between the methodaccording to the present invention and the prior art method.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

With imperfect CSI, the transmitter cannot generate a multiuserprecoding matrix that exactly matches the multiuser channel. Suchmismatch leads to additional CCI among users, which increases with thetransmission power and seriously restricts the performance of MU-MIMO,especially at high SNRs. The CCI caused by CSI error cannot besuppressed at a receiver by common interference-suppression methods suchas maximum-likelihood (ML) and MMSE detection. The effect is similar toan increase in additive noise. The basic idea of the present inventionis to study the relationship between such CCI level and theprecoding/scheduling results, and to use this relationship to adjust theprecoding and scheduling algorithms to better suit the imperfect CSIenvironment. According to the present invention, a method of multiuserprecoding and scheduling is provided to better control the CCI level dueto CSI error and improve MU-MIMO performance with imperfect CSI at thetransmitter.

In the present invention, it is assumed that the statistical propertiesof CSI error are available at the transmitter. For multiuser precoding,a MMSE-type algorithm is generally used, due to its ability to balanceinterference and noise. Here, the CCI caused by CSI error may beregarded as AWGN(Additive White Gaussian Noise), and a relationshipbetween the average power of CSI error and a precoding/filtering matrixmay be established based on a variance matrix of CSI error. Then, theCCI level, precoding and filtering matrixes are optimized jointlyaccording to the MMSE criterion.

In the present invention, for multiuser scheduling, we adopt thecapacity-maximization criterion and select a user/mode subset with themaximum achievable sum capacity. Similarly, a relationship between theCSI-error-related CCI level and the user/mode selection may beestablished and used to adjust scheduling operation, by regarding theCCI as AWGN in estimating the sum capacity for each user/mode subset.

Hereinafter, the principle of the method of multiuser precoding andscheduling according to the present invention will be described indetail.

Channel Model

Consider a downlink of a multiuser MIMO system with N_(T) transmitantennas at the BS and K users receiving service from the same BS overthe same time-frequency resource, each with N_(R) receive antennas. (Tobe noted, K is the number of users served in the same time-slot andfrequency band by spatial processing. The total number of users in acell may be much larger than K).

Assuming frequency-flat fading for all users, a channel matrix for userk is denoted by H_(k)=[h_(k,n) ^((m))], where h_(k,n) ^((m)) is thefading coefficient between the nth transmit antenna and the mth receiveantenna of user k, The number of data streams dedicated to userk isdenoted by S_(k). It is always assumed that S_(k)≦N_(R) and Σ_(k=1)^(K)S_(k)≦N_(T) . The data vector x_(k) of a length S_(k) for user k isfirst multiplied with a N_(T)×S_(k) precoding matrix T_(k) to belinearly transformed to a symbol vector of a length N_(T) fortransmitting from N_(T) antennas. The length-N_(T) symbol vectors for Kusers are then linearly superimposed and transmitted into a channel fromthe antenna array simultaneously. Here, it is always assumed that theelements of x_(k) are independent and identically distributed (i.i.d.)with a mean of zero and a unit variance. The total transmission power isthen given by

$\begin{matrix}{P_{T} = {{\sum\limits_{k = 1}^{K}{E\left( {{T_{k}x_{k}}}^{2} \right)}} = {\sum\limits_{k = 1}^{K}\mspace{14mu} {{trace}\mspace{14mu} \left( {T_{k}^{H}T_{k}} \right)}}}} & (1)\end{matrix}$

For each user k, the received signal vector is

$\begin{matrix}{y_{k} = {{{H_{k}{\sum\limits_{k^{\prime} = 1}^{K}{T_{k^{\prime}}x_{k^{\prime}}}}} + n_{k}} = {{H_{k}T_{k}x_{k}} + {H_{k}{\sum\limits_{k^{\prime} \neq k}{T_{k^{\prime}}x_{k^{\prime}}}}} + n_{k}}}} & (2)\end{matrix}$

where n_(k) is a vector of samples of an AWGN process with a mean ofzero and a variance of σ²=N₀/12. Each user k then generates an estimate{circumflex over (x)}_(k) for x_(k) by multiplying y_(k) with aS_(k)×N_(R) filtering matrix B_(k), as given by the equation

$\begin{matrix}\begin{matrix}{{\hat{x}}_{k} = {B_{k}y_{k}}} \\{= {{B_{k}H_{k}{\sum\limits_{k^{\prime} = 1}^{K}{T_{k^{\prime}}x_{k^{\prime}}}}} + {B_{k}n_{k}}}} \\{= {{B_{k}H_{k}T_{k}x_{k}} + {B_{k}H_{k}{\sum\limits_{k^{\prime} \neq k}{T_{k^{\prime}}x_{k^{\prime}}}}} + {B_{k}n_{k}}}}\end{matrix} & (3)\end{matrix}$

In the equation (3), the filtering matrix B_(k) can be derived based onvarious criteria, such as MMSE. Based on the equation (3), the maximummutual information between {circumflex over (x)}_(k) and x_(k) is

$\begin{matrix}\begin{matrix}{C_{k} = {\sum\limits_{s = 1}^{s_{k}}{\log_{2}\left( {1 + {\sin \; r_{k,s}}} \right)}}} \\{= {\sum\limits_{s = 1}^{s_{k}}{\log_{2}\left( {1 + \frac{{{b_{k,s}^{H}H_{k}t_{k,s}}}^{2}}{{{b_{k,s}}^{2}\sigma^{2}} + {\sum\limits_{{({k^{\prime},s^{\prime}})} \neq {({k,s})}}{{b_{k,s}^{H}H_{k}t_{k^{\prime},s^{\prime}}}}^{2}}}} \right)}}}\end{matrix} & (4)\end{matrix}$

where sinr_(k,s) is the post-processingsignal-to-interference-plus-noise ratio (SINR) for the sth element ofx_(k), b_(k,s) represents the sth column of B_(k) ^(T) and t_(k,s)represents the sth column of T_(k). The sum mutual information of theoverall MU-MIMO systems is then

$\begin{matrix}{C = {\sum\limits_{k = 1}^{K}\; C_{k}}} & (5)\end{matrix}$

In the following, we will design the method of multiuser precoding andscheduling based on the above channel model. For the purpose of clarity,a simpler situation of to perfect CSI at the transmitter (the prior art)will be first introduced, and then the present invention for thesituation of imperfect CSI will be described.

Multiuser Precoding and Scheduling with Perfect CSI

Successive MMSE (S-MMSE) Multiuser Precoding

As mentioned above, in the present invention, the MMSE-type multiuserprecoding algorithm may be used, due to its ability to balanceinterference and noise (Here, the noise means both the CCI due to CSIerror and the AWGN noise). In particular, the successive MMSE (S-MMSE)algorithm is used, which is a simplified implementation of MMSE-typealgorithm in the case that each user has more than one receive antennas.

The basic principle of the MMSE-type algorithm is to find a set ofoptimal precoding matrix {T_(k)} and filtering matrix {B_(k)} accordingto the MMSE criterion:

$\begin{matrix}\begin{matrix}{\left( {\left\{ T_{k} \right\},\left\{ B_{k} \right\}} \right) = {\underset{\underset{B_{1}\mspace{14mu} \ldots \mspace{14mu} B_{K}}{T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{K}}\mspace{14mu}}{argmin}{E\left( {\sum\limits_{k = 1}^{K}{{x_{k} - {\hat{x}}_{k}}}^{2}} \right)}}} \\{= {\underset{\underset{B_{1}\mspace{14mu} \ldots \mspace{14mu} B_{K}}{T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{K}}\mspace{14mu}}{argmin}{{E\left( {\sum\limits_{k = 1}^{K}{{x_{k} - \begin{pmatrix}{{B_{k}H_{k}{\sum\limits_{k^{\prime} = 1}^{K}{T_{k^{\prime}}x_{k^{\prime}}}}} +} \\{B_{k}n_{k}}\end{pmatrix}}}^{2}} \right)}.}}}\end{matrix} & (6)\end{matrix}$

Equation (6) relates to joint optimization problem, and its solution isgenerally very complicated to obtain. The S-MMSE algorithm, on the otherhand, provides a simplified, sub-optimal solution to this problem byiterative operations:

Step 1 Initialize each B_(k) by generating a random s_(k)×N_(R) matrix;

Step 2 Based on the current {B_(k)}, calculate the optimal precodingmatrix {T_(k)} according to the MMSE criterion as

$\begin{matrix}{\left\lbrack {T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{K}} \right\rbrack = {H^{H}{B^{H}\left( {{{BHH}^{H}B^{H}} + {\beta \; I}} \right)}^{- 1}}} & (7) \\{{{{where}\mspace{14mu} H} = \left\lbrack {H_{1}^{T}H_{2}^{T}\mspace{14mu} \ldots \mspace{14mu} H_{K}^{T}} \right\rbrack^{T}},} & (8) \\{{B = \begin{bmatrix}B_{1} & \; & 0 \\\; & \ddots & \; \\0 & \; & B_{K}\end{bmatrix}},} & (9) \\{{{{and}\mspace{14mu} \beta} = {\frac{\sigma^{2}}{P_{T}}{trace}\mspace{14mu} \left( {BB}^{H} \right)}};} & (10)\end{matrix}$

Step 3 Based on {T_(k)} calculated above, update the filtering matrix{B_(k)} according to the MMSE criterion as

$\begin{matrix}{{B_{k} = {T_{k}^{H}{H_{k}^{H}\left( {{\sum\limits_{k^{\prime} = 1}^{K}{H_{k}T_{k^{\prime}}T_{k^{\prime}}^{H}H_{k}^{H}}} + {\sigma^{2}I}} \right)}^{- H}}};\mspace{14mu} {{{for}\mspace{14mu} k} = {1 \sim K}}} & (11)\end{matrix}$

Step 4 Repeat steps 2 and 3 until the Frobenius norm of the change in{T_(k)} and {B_(k)} drops below a pre-set threshold or the number ofiteration operations has reached a certain value;

Step 5 Normalize the final precoding matrix {T_(k)} by (P_(T)/Σ_(k=1)^(K)trace(T_(k)T_(k) ^(H)))^(1/2).

Multiuser Scheduling

The total user number in the communication system is denoted by N. Thescheduler selects a subset of users out of N users for multiusertransmission, and also decides the number of data streams for eachselected user. Denote by

possible scheduling results, and

can be represented by three parts: the number of the selected users K(

), a set of indexes for the selected users {n_(k)(

), k=1˜K(

)}with 1≦n_(k)(

)≦N, and the numbers of data streams for the selected users {S_(k)(

), k=1˜K(

)}.The scheduler searches over a set of

and selects the optimal one, denoted by

, according to a certain criterion. For example, with thecapacity-maximization criterion, the scheduler selects

according to

$\begin{matrix} & (12)\end{matrix}$

where

is a set of K that the scheduler searches over, b_(k,s)(

) and t_(k,s)(

) represents the sth columns of B_(k) ^(T)(

) and T_(k)(

), respectively, with B_(k)(

) and T_(k)(

) be the filtering and precoding matrixes for the kth user in

. Here, B_(k)(

) and T_(k)(

) are obtained through the above Steps 1˜5 by setting K=K(

), {S_(k)}={S_(k)(

)}, and {H_(k)}={H_(n) _(k) ₍

₎}. The size of

depends on the used scheduling strategy. For example, with full searchscheduling,

includes all possibilities of

. Once

is selected, the {S_(k)(

),k=1˜K(

)} number of data streams are transmitted to the K(

) number of users over the same time-frequency resource by multiuserprecoding with the precoding matrixes {T_(k()

_(),k=)1˜K(

)}.

Multiuser Precoding and Scheduling with Imperfect CSI According to thePresent Invention

Denote by H _(k) the imperfect channel matrix of user k available at theBS, and {tilde over (H)}_(k)=H_(k)− H _(k) the CSI error for user k.Assume that the elements in {{tilde over (H)}_(k)} are random variablesof i.i.d. with a mean of zero and a variance of {tilde over (σ)}². The{{tilde over (H)}_(k)} can be attributable to various factors, such asfeedback quantization, feedback delay and the like. Also assume that thevalue of {tilde over (σ)}² is available at the transmitter. The value of{tilde over (σ)}² can be obtained by various measures such as measuringchannel estimation error, feedback error, and quantization error, etc.,and then feeding back from the receiver to the transmitter.

CCI-Estimation-Aided S-MMSE Precoding

With imperfect CSI, the channel model in the equation (3) is modified to

$\begin{matrix}{{\hat{x}}_{k} = {{{B_{k}\left( {{\overset{\_}{H}}_{k} + {\overset{\sim}{H}}_{k}} \right)}{\sum\limits_{k^{\prime} = 1}^{K}{T_{k^{\prime}}x_{k^{\prime}}}}} + {B_{k}n_{k}}}} & (13)\end{matrix}$

For the optimization of filtering matrixes {B_(k)} with given {T_(k)},the equation (13) can be rewritten as

$\begin{matrix}\begin{matrix}{{\hat{x}}_{k} = {B_{k}\left( {{{\overset{\_}{H}}_{k}{\sum\limits_{k^{\prime} = 1}^{K}{T_{k^{\prime}}x_{k^{\prime}}}}} + {{\overset{\sim}{H}}_{k}{\sum\limits_{k^{\prime} = 1}^{K}{T_{k^{\prime}}x_{k^{\prime}}}}} + n_{k}} \right)}} \\{= {B_{k}\left( {{{\overset{\_}{H}}_{k}{\sum\limits_{k^{\prime} = 1}^{K}{T_{k^{\prime}}x_{k^{\prime}}}}} + n_{k}} \right)}}\end{matrix} & (14)\end{matrix}$

where η_(k)={tilde over (H)}_(k)Σ_(k′)T_(k′)x_(k′)+n_(k) is a term ofCCI-plus-noise including both the CCI due to CSI error and the AWGN. Byapproximating η_(k) as a vector of complex Gaussian noise, the optimal{B_(k)} can be generated as

$\begin{matrix}{B_{k} = {T_{k}^{H}{H_{k}^{H}\left( {{\sum\limits_{k^{\prime} = 1}^{K}{{\overset{\_}{H}}_{k}T_{k^{\prime}}T_{k^{\prime}}^{H}{\overset{\_}{H}}_{k}^{H}}} + {E\left( {n_{K}n_{k}^{H}} \right)}} \right)}^{- H}}} & (15)\end{matrix}$

where

$\begin{matrix}\begin{matrix}{{E\left( {n_{K}n_{k}^{H}} \right)} = {{E\left( {\sum\limits_{k^{\prime} = 1}^{K}{{\overset{\sim}{H}}_{k}T_{k^{\prime}}x_{k^{\prime}}x_{k^{\prime}}^{H}T_{k^{\prime}}^{H}{\overset{\sim}{H}}_{k}^{H}}} \right)} +}} \\{{{\sigma^{2}I}\overset{(a)}{\approx}{{E\left( {\sum\limits_{k^{\prime} = 1}^{K}{{\overset{\sim}{H}}_{k}T_{k^{\prime}}T_{k^{\prime}}^{H}{\overset{\sim}{H}}_{k}^{H}}} \right)} + {\sigma^{2}I}}}} \\{= {{\sum\limits_{k^{\prime} = 1}^{K}\begin{pmatrix}{\left( {{{vec}\left( T_{k^{\prime}} \right)}^{T} \otimes I_{N_{R}}} \right) \times \left( {I_{S_{k^{\prime}}} \otimes {\overset{\sim}{R}}_{k}} \right) \times} \\\left( {{vec}{\left( T_{k^{\prime}} \right)^{T} \otimes I_{N_{R}}}} \right)^{H}\end{pmatrix}} + {\sigma^{2}I}}}\end{matrix} & (16)\end{matrix}$

In the equation (16), (a) is derived by approximating each x_(k)x_(k)^(H) as I for all k, vec(A)=[a₁ ^(T) . . . a_(j) ^(T) . . . ]^(T) witha_(j) being the jth column of the matrix A,

denotes the Kronecker product, and {tilde over(R)}_(k)=E(vec(h_(k))vec({tilde over(H)}_(k))^({tilde over (H)}))={tilde over (σ)}²I_(N) _(R) _(×N) _(T) isthe covariance matrix of CSI error, vec({tilde over (H)}_(k)).

For the optimization of precoding matrixes {T_(k)} with given {B_(k)},the equation (13) can be rewritten in a compact form as

{circumflex over (x)}=B( H+{tilde over (H)})Tx+Bn=B HTx+B{tilde over(H)}Tx+Bn=B HTx+μ  (17)

where

{circumflex over (x)}=[{circumflex over (x)}₁ ^(T){circumflex over (x)}₂^(T) . . . {circumflex over (x)}_(K) ^(T)]^(T),  (18)

H=[ H ₁ ^(T) H ₂ ^(T) . . . H _(k) ^(T)]^(T),  (19)

{tilde over (H)}=[{tilde over (H)}₁ ^(T){tilde over (H)}₂ ^(T) . . .{tilde over (H)}_(K) ^(T)]^(T),  (20)

T=[T₁T₂ . . . T_(K)],  (21)

n=[n₁ ^(T)n₂ ^(T) . . . n_(K) ^(T)]^(T),  (22)

μ=B{tilde over (H)}Tx+Bn ,  (23)

and B is defined in the equation (9). Similarly, μ is a term ofCCI-plus-noise including both the CCI due to CSI error and the AWGN. Byapproximating μ as a vector of complex Gaussian noise, the optimal{T_(k)} can be generated as

$\begin{matrix}{\left\lbrack {T_{1}\mspace{14mu} \ldots \mspace{14mu} T_{K}} \right\rbrack = {{\overset{\_}{H}}^{H}{B^{H}\left( {{B{\overset{\_}{HH}}^{H}B^{H}} + {\frac{1}{P_{T}}{E\left( {uu}^{H} \right)}}} \right)}^{- 1}}} & (24)\end{matrix}$

where

$\begin{matrix}\begin{matrix}{{E\left( {uu}^{H} \right)} = {{E\left( {B\overset{\sim}{H}{Txx}^{H}T^{H}{\overset{\sim}{H}}^{H}B^{H}} \right)} + {\sigma^{2}{{trace}\left( {BB}^{H} \right)}I}}} \\{\overset{(a)}{\approx}{{P_{T}{E\left( {B\overset{\sim}{H}{\overset{\sim}{H}}^{H}B^{H}} \right)}} + {\sigma^{2}{{trace}\left( {BB}^{H} \right)}I}}} \\{= {{P_{T}B\overset{\sim}{R}B^{H}} + {\sigma^{2}{{trace}\left( {BB}^{H} \right)}I}}}\end{matrix} & (25)\end{matrix}$

In the equation (25), (a) is derived by approximating Txx^(H)T^(H) asP_(T)I, and {tilde over (R)}=E({tilde over (H)}{tilde over(H)}^(H))={tilde over (σ)}²I_(N) _(R) _(×K) is the covariance matrix of{tilde over (H)}, which can be calculated from the covariance matrix{{tilde over (R)}_(k)} of CSI error.

By replacing the equations (7) and (11) in the above Steps 1˜5 with theequations (15) and (24), the CCI-estimation-aided S-MMSE precodingalgorithm of the present invention can be obtained.

CCI-Estimation-Aided Multiuser Scheduling

The channel model in the equation (13) can be rewritten for imperfectCSI as

$\begin{matrix}{{\hat{x}}_{k} = {{B_{k}{\overset{\_}{H}}_{k}T_{k}x_{k}} + {B_{k}{\overset{\_}{H}}_{k}{\sum\limits_{k^{\prime} = k}{T_{k^{\prime}}x_{k^{\prime}}}}} + {B_{k}n_{k}}}} & (26)\end{matrix}$

Then, for each user/mode subset K, the scheduler estimates theachievable capacity by approximating B_(k)η_(k) as a vector of complexGaussian noise, as given in the equation

$\begin{matrix}\begin{matrix}{{C{()}} = {\sum\limits_{k = 1}^{K{()}}\; {\sum\limits_{s = 1}^{S_{k}{()}}\frac{{{{b_{k,s}^{H}{()}}{\overset{\_}{H}}_{{nk}{()}}{t_{k,s}{()}}}}^{2}}{\begin{matrix}{{\sum\limits_{{({k^{\prime},s^{\prime}})} \neq {({k,s})}}\; {{{b_{k,s}^{H}{()}}{\overset{\_}{H}}_{n_{k{()}}}{t_{k^{\prime}s^{\prime}}{()}}}}^{2}} +} \\{E\left( {{{b_{k,s}^{H}{()}}{n_{k}{()}}}}^{2} \right)}\end{matrix}}}}} \\{= {\sum\limits_{k = 1}^{K{()}}\; {\sum\limits_{s = 1}^{S_{k}{()}}\frac{{{{b_{k,s}^{H}{()}}{\overset{\_}{H}}_{{nk}{()}}{t_{k,s}{()}}}}^{2}}{\begin{matrix}{{\sum\limits_{{({k^{\prime},s^{\prime}})} \neq {({k,s})}}\; {{{b_{k,s}^{H}{()}}{\overset{\_}{H}}_{n_{k{()}}}{t_{k^{\prime}s^{\prime}}{()}}}}^{2}} +} \\{{b_{k,s}^{H}{()}}{E\left( {{n_{k}{()}}{n_{k}^{H}{()}}} \right)}{b_{k,s}{()}}}\end{matrix}}}}}\end{matrix} & (27)\end{matrix}$

where

${n_{k}{()}} = {{{\overset{\sim}{H}}_{n_{k{()}}}{\sum\limits_{k^{\prime} = 1}^{K{()}}{{T_{k^{\prime}}{()}}x_{k^{\prime}}}}} + n_{k}}$

and E(η_(k)(

)η_(k) ^(H)(

)) can be calculated with the equation (16). Then, theCCI-estimation-aided scheduling algorithm selects {circumflex over (K)}according to the following criterion

$\begin{matrix}{= {\underset{\in}{argmax}{{C{()}}.}}} & (28)\end{matrix}$

Hereinafter, the preferred embodiments of the present invention will bedescribed in detail by referring to the drawings.

FIG. 1 is a flow chart of a method of multiuser precoding and schedulingaccording to the present invention.

According to the present invention, in step 101, CSI (which may be notaccurate due to various factors such as feedback quantization, feedbackdelay) and statistic properties of CSI error are fed back to the BS fromthe UE. The CSI is an estimation of a channel matrix, and the statisticproperties of CSI error are those of an error matrix of the estimationof the channel matrix. For example, as illustrated above, the estimationof the channel matrix may be H _(k), and the statistic properties of theCSI error may be the covariance matrix {{tilde over (R)}_(k)}. In step103, the multiuser precoding matrix and scheduling scheme are generatedby the BS, according to the feedback CSI and the statistic properties ofCSI error. As illustrated above. the multiuser precoding may use theMMSE-type algorithm, and the multiuser scheduling may use thecapacity-maximization criterion. Finally, in step 105, user data aremultiuser precoded and scheduled by using the generated multiuserprecoding matrix and scheduling scheme.

FIG. 2 is a block diagram of the BS for implementing the method ofmultiuser precoding and scheduling according to the present invention.As shown in FIG. 2, the BS comprises a reception device 201, a multiuserprecoding matrix and scheduling scheme generation device 203, and amultiuser precoding and scheduling device 205. The reception device 201receives the CSI and the statistic properties of CSI error which are fedback from the UE. The multiuser precoding matrix and scheduling schemegeneration device 203 generates the multiuser precoding matrix andscheduling scheme, according to the feedback CSI and the statisticproperties of CSI error. The multiuser precoding and scheduling device205 performs multiuser precoding and scheduling on the user data byusing the generated multiuser precoding matrix and scheduling scheme.

FIGS. 3 and 4 are graphs showing performance comparison between themethod according to the present invention and the prior art method. Asshown in FIGS. 3 and 4, the CCI-estimation-aided MU-MIMO schemeaccording to the present invention is compared with the conventionalMU-MIMO scheme based on the original S-MMSE and MET algorithms withgreedy scheduling strategy. Here, 4 transmit antennas are arranged atthe base station, 2 receive antennas per user, and the total number ofusers is 4. The elements in channel matrixes {H_(k)} are modeled ascomplex white Gaussian variables of i.i.d. with a mean of zero and aunit variance. The elements in CSI error matrixes {{tilde over (H)}_(k)}are modeled as complex white Gaussian noises of i.i.d. with a mean ofzero and a variance of {tilde over (σ)}². In the present invention,{tilde over (σ)}² is set to be 0.1 and 0.5 in FIGS. 3 and 4,respectively. Both MMSE and non-MMSE receivers are considered. As can beseen, the proposed scheme in the present invention outperforms MET andS-MMSE with greedy scheduling for both MMSE and non-MMSE receiver,especially when the CSI error is high.

The present invention has the following advantages.

1. It greatly improves the system performance of MU-MIMO when imperfectCSI is used at the transmitter(this is a realistic situation inpractice).

2. It brings only marginal additional complexity at the BS and noadditional complexity at UEs.

3. It is flexible with respect to causes of CSI error, such as channelestimation error, quantization error, feedback error, etc, and can beused with various MU-MIMO mechanisms, for example, based on sounding andfeedback.

In summary, MU-MIMO operation is a hot topic in many broadband radiocommunication standards, such as IEEE 802.16 and 3GPP LTE, due to itsgreat potential to improve cell throughput. The CSI error at thetransmitter is one of the practical problems that restrict theapplication of MU-MIMO in real systems. The solution provided by thepresent invention can bring apparent advantages at the cost of marginaladditional complexity at the BS.

The above is only the preferred embodiments of the present invention andthe present invention is not limited to the above embodiments.Therefore, any modifications, substitutions and improvements to thepresent invention are possible without departing from the spirit andscope of the present invention.

1. A method of multiuser precoding and scheduling, comprising: feedingchannel state information (CSI) and statistic properties of CSI errorback to a base station (BS) from a user equipment (UE); generating, atthe BS, multiuser precoding matrix and scheduling scheme according tothe feedback CSI and the statistic properties of CSI error; andperforming multiuser precoding and scheduling on user data by using thegenerated multiuser preceding matrix and scheduling scheme.
 2. Themethod according to the claim 1, wherein the CSI is an estimation of achannel matrix.
 3. The method according to claim 1, wherein thestatistic properties of CSI error comprise a covariance matrix of errorof the estimation of the channel matrix.
 4. The method according toclaim 1, wherein the multiuser precoding uses aminimum-mean-square-error (MMSE)-type algorithm.
 5. The method accordingto claim 4, wherein the MMSE-type algorithm is a successive MMSEalgorithm.
 6. The method according to claim 1, wherein the multiuserscheduling uses a capacity-maximization criterion.
 7. The methodaccording to claim 1, wherein the statistic properties of the CSI errorare obtained by measuring channel estimation error, feedback error andquantization error, and then fed back to a transmitter from a receiver.8. The method according to claim 1, wherein the method is used in anmultiuser multiple input multiple output (MU-MIMO) communication system.9. A base station comprising: a reception device for receiving CSI andstatistic properties of CSI error which are fed back from a userequipment (UE); a multiuser precoding matrix and scheduling schemegeneration device for generating multiuser precoding matrix andscheduling scheme according to the to feedback CSI and the statisticproperties of CSI error; and a multiuser precoding and scheduling devicefor performing multiuser precoding and scheduling on user data by usingthe generated multiuser precoding matrix and scheduling scheme.
 10. Thebase station according to claim 9, wherein the CSI is an estimation of achannel matrix.
 11. The base station according to claim 9, wherein thestatistic properties of CSI error comprise a covariance matrix of errorof the estimation of the channel matrix.
 12. The base station accordingto claim 9, wherein the multiuser precoding uses aminimum-mean-square-error (MMSE)-type algorithm.
 13. The base stationaccording to claim 12, wherein the MMSE-type algorithm is a successiveMMSE algorithm.
 14. The base station according to claim 9, wherein themultiuser scheduling uses a capacity-maximization criterion.
 15. Thebase station according to claim 9, wherein the statistic properties ofthe CSI error are obtained by measuring channel estimation error,feedback error and quantization error, and then fed back to atransmitter from a receiver.
 16. The base station according to claim 9,wherein the base station is used in a multiuser multiple input multipleoutput (MU-MIMO) communication system.